Residential Area Recognition Using Oscillatory Correlation Segmentation of Hyperspectral Imagery

Shi, Bingjie; Liu, Chun; Sun, Weiwei; Wu, Hangbin

doi:10.1109/isidf.2011.6024292

Cited by 1 publication

(1 citation statement)

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“…Jin et al [16] presented a residential area recognition method for some remote sensing images based on Fourier transformation and Hough transformation. Shi et al [17] proposed an extended oscillatory correlation algorithm to perform unsupervised scene recognition of residential areas for hyperspectral imagery. Experiment demonstrated the utility of the proposed method for residential areas recognition.…”

Section: Introductionmentioning

confidence: 99%

A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

Wan

Wang

et al. 2016

Computational Intelligence and Neuroscience

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Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

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Section: Introductionmentioning

confidence: 99%